All known active life requires liquid water. The correlation between liquid water and the presence of life on Earth has guided the search for life on other planets. For terrestrial-like life to exist in the harsh conditions that dominate the surfaces of other rocky planets, the minimum fundamental requirements of liquid water, nutrients, and chemical energy must be met. Within our solar system, Mars is a strong candidate for hospitable environments able to support life, due to the reservoirs of water within its crust and the strong likelihood of liquid water. The aim of this thesis is to refine the search for liquid water and environments that may be hospitable to life on Mars. Two complementary methodologies are developed and utilised to achieve this aim. Water requires a relatively narrow range of pressures and temperatures to occur in the liquid phase. The first approach of this thesis compares this range with the pressure-temperature conditions that occur within the Earth, the Earth’s active biosphere, and Mars. Temperature, pressure and water activity are examined to determine the extent to which they restrict life from some liquid water environments. The relevant thresholds are then applied to Mars and compared to models of where liquid water environments are likely to occur under present-day martian conditions. Extensive regions of the Earth may be inhospitable despite lying within the hydrosphere. Life is likely restricted from ~ 81% of the volume of the hydrosphere of Earth due to high temperature and/or low water activity. In contrast, the fraction of Mars that can support liquid water is five times larger than that of Earth, given estimates of an average martian brine. Many environments within the martian crust can potentially support life, with perennially habitable conditions extending from approximately 10 to 37 km beneath the surface. The surface and shallow regolith may also be habitable in the warmest regions of the planet. The second approach focuses on the shallow subsurface of Mars within the top ~20 m. The thermal behaviour of surface materials determines the occurrence of transient shallow liquid water and habitable temperatures for life. Ten classes of surface materials are identified from analysis of global martian thermal inertia and albedo, through the technique of algorithmic classification. These classes are interpreted as mixtures of dust, sand, duricrust, rocks and ice on the surface, and validated through comparisons with independent datasets. Low latitude locations of dark sand, duricrust and pebbles in Syrtis Major, Oxia Palus, Mawrth Vallis and eastern Meridiani Planum are identified as having high potential for hospitable liquid water environments at < 10 m depth. Dark, coarse, sand dominated surfaces are found in Syrtis Major and Aram Chaos and are predicted to be locations of low volume flows of liquid water, potentially analogous to the observed martian recurring slope lineae. This thesis identifies where habitable liquid water environments may occur on Mars, strengthening the astrobiological significance of the planet and providing direction for future robotic and satellite missions searching for life.

Two complementary approaches in refining the search for liquid water and habitable environments on present-day Mars

Conventional unsupervised classification divides all pixels within an image into corresponding classes based on the distance between pixels and the cluster centres. The number of classes must be selected a priori but is seldom ascertainable with little information. To analyze a large dataset, such as a remote sensing dataset, requires an automatic unsupervised classifier which needs no human effort during the process of image clustering. A genetic algorithm (GA) is adopted to search the cluster centres and choose a suitable cluster number for digital images to overcome the disadvantages of the conventional unsupervised classifier. The GA-based automated classifier was executed on several test images for validity and SPOT satellite imagery for practical application. The satellite images classified by the GA-based classifier and iterative self-organizing data analysis technique (ISODATA) were compared with a classified result through a supervised classification. According to the estimation of classification...

/pdf/a-genetic-algorithm-ga-based-automated-classifier-for-remote-3t28qrnp0r.pdf

A genetic algorithm (GA) based automated classifier for remote sensing imagery

Thermal inertia and albedo provide information on the distribution of surface materials on Mars. These parameters have been mapped globally on Mars by the Thermal Emission Spectrometer (TES) onboard the Mars Global Surveyor. Two-dimensional clusters of thermal inertia and albedo reflect the thermophysical attributes of the dominant materials on the surface. In this paper three automated, non-deterministic, algorithmic classification methods are employed for defining thermophysical units: Expectation Maximisation of a Gaussian Mixture Model; Iterative Self-Organizing Data Analysis Technique (ISODATA); and Maximum Likelihood. We analyse the behaviour of the thermophysical classes resulting from the three classifiers, operating on the 2007 TES thermal inertia and albedo datasets. Producing a rigorous mapping of thermophysical classes at ~3 km/pixel resolution remains important for constraining the geologic processes that have shaped the Martian surface on a regional scale, and for choosing appropriate landing sites. The results from applying these algorithms are compared to geologic maps, surface data from lander missions, features derived from imaging, and previous classifications of thermophysical units which utilized manual (and potentially more time consuming) classification methods. These comparisons comprise data suitable for validation of our classifications. Our work shows that a combination of the algorithms—ISODATA and Maximum Likelihood—optimises the sensitivity to the underlying dataspace, and that new information on Martian surface materials can be obtained by using these methods. We demonstrate that the algorithms used here can be applied to define a finer partitioning of albedo and thermal inertia for a more detailed mapping of surface materials, grain sizes and thermal behaviour of the Martian surface and shallow subsurface, at the ~3 km scale.

/pdf/an-alternative-approach-to-mapping-thermophysical-units-from-2c9im4k5yw.pdf

An Alternative Approach to Mapping Thermophysical Units from Martian Thermal Inertia and Albedo Data Using a Combination of Unsupervised Classification Techniques

Recently the high-resolution satellite images are helpfully using the land cover, status data for the natural resources or environment management. The effective satellite analysis process for these satellite images that require high investment can be increase the effectiveness has become increasingly important. In this Study, the statistical value of the training data is calculated and analyzed during the preprocessing. Also, that is explained about the maximum likelihood classification of traditional classification method, artificial neural network (ANN) classification method and Support Vector Machines(SVM) classification method and then the IKONOS high-resolution satellite imagery was produced the land cover map using each classification method. Each result data had to analyze the accuracy through the error matrix. The results of this study prove that SVM classification method can be good alternative of the total accuracy of about 86% than other classification method.

/pdf/an-analysis-of-land-cover-classification-methods-using-m3jenujmbh.pdf

An Analysis of Land Cover Classification Methods Using IKONOS Satellite Image

NASA, NOAA and UPRM for granting me the opportunity and support to obtain my masters degree.

Algorithms for image classification using wavelets and fusion methods

A new methodology is presented for classifying remotely-sensed imagery. This technique is meant to be locally-adaptive, supports non-gaussian statistics, and still allows one to generate an automatic classification. The new methodology requires training data, just as the standard technique does, but it uses an unsupervised technique (ISODATA) with which to first classify the data. The clusters from the unsupervised step are used with the training data in a supervised classification to get the mapping from cluster to class. Often, the statistics of the classification procedure are ill-conditioned for large feature spaces, and so this new methodology is designed for multi-step classifications. The idea is for the analyst to break up the classification into two or more steps where more general classes are separated first. The automated procedure then determines which subset of all the features are necessary at each step of the process. At the moment this is implemented using an exhaustive search stategy, but other methods are possible and will be explored. The resulting classification reports which channels were important at each stage of the classification process, thus automating the first step in understanding how and why the classification process works. In combination with a simple, unsupervised segmentation algorithm, which is also presented, this technique is then applied to SIR-C/X-SAR data.

An automated unsupervised/supervised classification methodology

SPRI, the Simulator of Polarimetric Radar Images, is a suite of PCI image processing programs which together can be used to simulate millimetre wave (MMW) radar images of terrain. SPRI is intended to produce images similar to existing imagery but with one or more significant changes to facilitate comparisons. These significant changes include radar frequency, angle of incidence, season of year, recent precipitation, etc. The heart of the simulator package is a scattering matrix generator which calls upon a data base of measured Mueller matrices of terrain. This generator program produces high-resolution single-look polarimetric images. Simulated images can be degraded in polarization, number of looks, resolution, etc. to match those of a particular radar sensor. Auxiliary programs have been written to help adapt existing images to the frequency and incidence angle desired. For example, a sub-suite of programs classifies shadows and manipulates them for incidence angle changes. The simulator relies upon a data base of over 2500 Mueller matrices measured at 35 and 95 GHz by instrumentation grade scatterometers. The data are organized by frequency, incidence angle, and terrain type, and are accompanied by extensive ground truth. This data base is a research tool in its own right, and as such has a password-protected Web interface.

MMW polarimetric data base of terrain reflectivity and an associated image simulator

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An automated unsupervised/supervised classification methodology

MMW polarimetric data base of terrain reflectivity and an associated image simulator